Journal ArticleDOI
A deep learning framework for text-independent writer identification
Malihe Javidi,Mahdi Jampour +1 more
Reads0
Chats0
TLDR
This work proposes an end-to-end system that relies on a straightforward yet well-designed deep network and very efficient feature extraction, emphasizing feature engineering, and empirically demonstrates that the conjugated network outperforms the original ResNet and can work well for real-world applications in which patches with few letters exist.About:
This article is published in Engineering Applications of Artificial Intelligence.The article was published on 2020-10-01. It has received 25 citations till now. The article focuses on the topics: Handwriting & Feature engineering.read more
Citations
More filters
Journal ArticleDOI
Deep-learning-based short-term electricity load forecasting: A real case application
TL;DR: In this paper , a new method that uses one-dimensional CNNs based on Video Pixel Networks (VPNs) for short-term load forecasting, in which the gating mechanism of Multiplicative Units of the VPNs is modified in some sense, for short term load forecasting.
Journal ArticleDOI
GR-RNN : Global-Context Residual Recurrent Neural Networks for Writer Identification
Sheng He,Lambert Schomaker +1 more
TL;DR: An end-to-end neural network system to identify writers through handwritten word images, which jointly integrates global-context information and a sequence of local fragment-based features, and can provide state-of-the-art performance.
Journal ArticleDOI
A graph-based solution for writer identification from handwritten text
Atta Rahman,Zahid Halim +1 more
Journal ArticleDOI
Writer identification using redundant writing patterns and dual-factor analysis of variance
TL;DR: Wang et al. as mentioned in this paper used redundant writing patterns and dual-factor analysis of variance (DF-ANOVA) to confirm the writer based on a very small amount of available text.
Journal ArticleDOI
User Authentication Based on Handwriting Analysis of Pen-Tablet Sensor Data Using Optimal Feature Selection Model
TL;DR: In this article, the authors proposed a robust and efficient user identification system using an optimal feature selection technique based on the features from the sensor's signal of pen and tablet devices, which includes more genuine and accurate numerical data which are used for features extraction model based on both the kinematic and statistical features of individual handwritings.
References
More filters
Proceedings ArticleDOI
Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Book ChapterDOI
Identity Mappings in Deep Residual Networks
TL;DR: In this paper, the forward and backward signals can be directly propagated from one block to any other block, when using identity mappings as the skip connections and after-addition activation.
Proceedings ArticleDOI
Convolutional neural networks at constrained time cost
Kaiming He,Jian Sun +1 more
TL;DR: This paper investigates the accuracy of CNNs under constrained time cost, and presents an architecture that achieves very competitive accuracy in the ImageNet dataset, yet is 20% faster than “AlexNet” [14] (16.0% top-5 error, 10-view test).
Journal ArticleDOI
The IAM-database: an English sentence database for offline handwriting recognition
Urs-Viktor Marti,Horst Bunke +1 more
TL;DR: A database that consists of handwritten English sentences based on the Lancaster-Oslo/Bergen corpus, which is expected that the database would be particularly useful for recognition tasks where linguistic knowledge beyond the lexicon level is used.
Journal ArticleDOI
Writer identification using directional ink-trace width measurements
TL;DR: The Quill feature is a probability distribution of the relation between the ink direction and the ink width that illustrates that ink width patterns are valuable and is already being used by domain experts using a graphical interface.